Image processing device

ABSTRACT

An image processing device for performing a data decompression is provided. The image processing device includes a decoder circuit having a plurality of stages for decompressing compressed image data of a plurality of pixels. The decoder circuit is configured to divide the pixels into a plurality of groups. The first stage performs prediction compensation on the compressed image data of a first pixel of the first group at a first time to generate first prediction data, and performs the prediction compensation on the compressed image data of a second pixel of the first group at a second time using the first prediction data. The second stage performs the prediction compensation on the compressed image data of a first pixel of the second group at the second time using the first prediction data, to generate second prediction data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/252,796 filed Jan. 21, 2019, which claims the benefit of priorityunder 35 U.S.C. 119 to Korean Patent Application No. 10-2018-0010162,filed on Jan. 26, 2018, and Korean Patent Application No.10-2018-0041788, filed on Apr. 10, 2018, in the Korean IntellectualProperty Office, the disclosures of which are incorporated by referencein their entireties herein.

BACKGROUND 1. Technical Field

The present disclosure relates to an image processing device.

2. Discussion of Related Art

More and more applications demand high-resolution video images andhigh-frame rate images. Accordingly, the amount of data accesses from amemory (i.e., bandwidth) storing these images by various multimediaintellectual property (IP) blocks of image processing devices hasgreatly increased.

Each image processing device has limited processing capability. When thebandwidth increases, the processing capability of the image processingdevice may reach this limit. Accordingly, a user of the image processingdevice may experience a decrease in speed while recording or playing avideo image.

SUMMARY

At least one embodiment of the present inventive concept provides animage processing device having improved processing speed.

According to an exemplary embodiment of the present inventive concept,there is provided an image processing device for performing a datadecompression. The image processing device includes a decoder circuithaving a plurality of stages for decompressing first compressed imagedata of a plurality of pixels to original image data. The stages includeat least first and second stages. The decoder circuit is configured todivide the pixels into a plurality of groups comprising at least firstand second groups that are adjacent one another. The first stageperforms prediction compensation on the first compressed image data of afirst pixel of the first group at a first time to generate firstprediction data, and performs the prediction compensation on the firstcompressed image data of a second pixel of the first group at a secondtime using the first prediction data. The second stage performs theprediction compensation on the first compressed image data of a firstpixel of the second group at the second time using the first predictiondata, to generate second prediction data.

According to an exemplary embodiment of the present inventive concept,there is provided a method of decompressing first compressed image dataof a plurality of pixels to original image data. The method includes:dividing the pixels into a plurality of groups comprising at least firstand second groups that are adjacent one another; performing predictioncompensation on the first compressed image data of a first pixel of thefirst group at a first time to generate first prediction data;performing the prediction compensation on the first compressed imagedata of a second pixel of the first group at a second time using thefirst prediction data; and performing the prediction compensation on thefirst compressed image data of a first pixel of the second group at thesecond time using the first prediction data, to generate secondprediction data.

According to an exemplary embodiment of the present inventive concept,there is provided an image processing device for performing a datacompression. The device includes an encoder circuit having a pluralityof stages for compressing original image data of a plurality of pixelsto first compressed image data. The stages include at least first andsecond stages. The encoder circuit is configured to divide the pixelsinto a plurality of groups comprising at least first and second groupsthat are adjacent one another. The first stage processes the originalimage data of a first pixel of the first group at a first time togenerate first prediction data, and processes the original image data ofa second pixel of the first group and the first prediction data at asecond time to generate first residual data. The first compressed imagedata includes the first prediction data, the first residual data, andthe second residual data.

According to an exemplary embodiment of the present inventive concept,there is provided a method of compressing original image data of aplurality of pixels. The method includes: dividing the pixels into aplurality of groups comprising at least first and second groups that areadjacent one another; processing the original image data of a firstpixel of the first group at a first time to generate first predictiondata; processing the original image data of a second pixel of the firstgroup and the first prediction data at a second time to generate firstresidual data; processing the original image data of a first pixel ofthe second group and the first prediction data at the second time togenerate to generate second residual data; and generating compressedimage data including the first prediction data, the first residual data,and the second residual data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become apparent by describing in detailexemplary embodiments thereof with reference to the accompanyingdrawings in which:

FIG. 1 is a block diagram of an image processing device according to anexemplary embodiment of the inventive concept;

FIG. 2 is a detailed block diagram of a frame buffer compressor (FBC)illustrated in FIG. 1 ;

FIG. 3 is a detailed block diagram of an encoder illustrated in FIG. 2 ;

FIG. 4 is a detailed block diagram of a decoder illustrated in FIG. 2 ;

FIG. 5 illustrates the arrangement of pixels of image data of the imageprocessing device according to the embodiments;

FIG. 6 illustrates pixels in a top row to explain the prediction of theimage processing device according to the embodiments:

FIG. 7 illustrates, in a time-series manner, the order in whichprediction compensation is performed on the pixels of FIG. 6 ;

FIG. 8 illustrates pixels in a row other than the top row to explain theprediction of the image processing device according to an exemplaryembodiment of the inventive concept:

FIG. 9 illustrates, in a time-series manner, the order in whichprediction compensation is performed on the pixels of FIG. 8 ;

FIG. 10 illustrates pixels in a row other than a top row to explain theprediction of an image processing device according to exemplaryembodiment of the inventive concept;

FIG. 11 illustrates the arrangement of pixels to explain predictionperformed within a group by an image processing device according to anexemplary embodiment of the inventive concept;

FIG. 12 is a detailed block diagram of a prediction module of the imageprocessing device according to an exemplary embodiment of the inventiveconcept;

FIG. 13 is a detailed diagram of a branch statement illustrated in FIG.12 ;

FIG. 14 is a conceptual diagram for structurally explaining theoperation of the branch statement of FIG. 13 ;

FIG. 15 illustrates a lookup table of FIG. 12 ;

FIG. 16 is a detailed diagram of a prediction equation illustrated inFIG. 12 ;

FIG. 17 is a block diagram of an image processing device according to anexemplary embodiment of the inventive concept; and

FIG. 18 is a block diagram of an image processing device according to anexemplary embodiment of the inventive concept.

DETAILED DESCRIPTION

An image processing device according to an exemplary embodiment of theinventive concept will now be described with reference to FIGS. 1through 9 .

FIG. 1 is a block diagram of an image processing device according toembodiments.

Referring to FIG. 1 , the image processing device according to theembodiments includes a multimedia intellectual property (IP) 100 (e.g.,an IP core, and IP block, a circuit, etc.), a frame buffer compressor(FBC) 200 (e.g., a circuit, a digital signal processor, etc.), a memory300, and a system bus 400.

In an exemplary embodiment, the multimedia IP 100 is part of the imageprocessing device that directly executes the image processing of theimage processing device. The multimedia IP 100 may include a pluralityof modules for performing image recording and reproduction such ascamcording, playback, etc. of video images.

The multimedia IP 100 receives first data (e.g., image data) from anexternal device such as a camera and converts the first data into seconddata. For example, the first data may be raw moving image data or rawstatic image data. The second data may be data generated by themultimedia IP 100 and may also include data resulting from themultimedia IP 100 processing the first data. The multimedia IP 100 mayrepeatedly store the second data in the memory 300 and update the seconddata through various steps. The second data may include all of the dataused in these steps. The second data may be stored in the memory 300 inthe form of third data. Therefore, the second data may be data beforebeing stored in the memory 300 or after being read from the memory 300.

In an exemplary embodiment, the multimedia IP 100 includes an imagesignal processor (ISP) 110, a shake correction module (G2D) 120, amulti-format codec (MFC) 130, a graphics processing unit (GPU) 140, anda display 150. However, the present inventive concept is not limited tothis case. That is, the multimedia IP 100 can include at least one ofthe ISP 110, the G2D 120, the MFC 130, the GPU 140, and the display 150described above. In other words, the multimedia IP 100 may beimplemented by a processing module that has to access the memory 300 inorder to process data representing a moving image or a static image.

The ISP 110 receives the first data and converts the first data into thesecond data by pre-processing the first data. In an embodiment, thefirst data is image source data in an RGB format. For example, the ISP110 may convert the first data in the RGB format into the second data ina YUV format.

The RGB format refers to a data format in which colors are representedbased on three primary colors of light. That is, an image is representedusing three kinds of colors, i.e., red, green, and blue. On the otherhand, the YUV format refers to a data format in which brightness, i.e.,a luma signal and a chroma signal are represented separately. That is. Ydenotes a luma signal, and U(Cb) and V(Cr) denote chroma signals,respectively. U denotes a difference between the luma signal and a bluesignal component, and V denotes a difference between the luma signal anda red signal component.

Data in the YUV format may be obtained by converting the RGB type datausing a conversion formula. For example, a conversion formula such asY=0.3R+0.59G+0.11B, U=(B−Y)×0.493, V=(R−Y)×0.877 may be used to convertthe RGB type data into the YUV type data.

Since the human eye is sensitive to luma signals but less sensitive tocolor signals, it may be easier to compress data in the YUV format thanto compress data in the RGB format. Therefore, the ISP 110 may convertthe first data in the RGB format into the second data in the YUV format.

After the ISP 110 converts the first data into the second data, itstores the second data in the memory 300.

The G2D 120 may perform shake correction of static image data or movingimage data. The G2D 120 may read the first data or the second datastored in the memory 300 to perform shake correction. In an embodiment,the shake correction refers to detecting shakes of a camera in movingimage data and removing the shakes from the moving image data.

The G2D 120 may generate new second data or update the second data bycorrecting shakes in the first data or the second data and may store thegenerated or updated second data in the memory 300.

The MFC 130 may be a codec for compressing moving image data. Generally,moving image data is very large in size. Therefore, a compression modulefor reducing the size of the moving image data is needed. The movingimage data may be compressed based on the associative relationshipbetween a plurality of frames, and this compression may be performed bythe MFC 130. The MFC 130 may read and compress the first data or mayread and compress the second data stored in the memory 300.

The MFC 130 may generate new second data or update the second data bycompressing the first data or the second data and store the new seconddata or the updated second data in the memory 300.

The GPU 140 may perform an arithmetic process to calculate and generatetwo-dimensional or three-dimensional graphics. The GPU 140 may calculatethe first data or calculate the second data stored in the memory 300.The GPU 140 may be specialized in processing graphics data and mayprocess graphics data in parallel.

The GPU 140 may generate new second data or update the second data bycompressing the first data or the second data and store the new seconddata or the updated second data in the memory 300.

The display 150 may display the second data stored in the memory 300 ona screen. The display 150 may display image data. i.e., the second dataprocessed by other components of the multimedia IP 100, that is, the ISP110, the G2D 120, the MFC 130 and the GPU 140, on the screen. However,the present inventive concept is not limited to this case.

Each of the ISP 110, the G2D 120, the MFC 130, the GPU 140 and thedisplay 150 of the multimedia IP 100 may operate individually. That is,each of the ISP 110, the G2D 120, the MFC 130, the GPU 140, and thedisplay 150 may individually access the memory 300 to write or readdata.

In an embodiment, the FBC 200 converts the second data into the thirddata by compressing the second data before the elements of themultimedia IP 100 access the memory 300 individually. The FBC 200 maytransmit the third data to the multimedia IP 100, and the multimedia IP100 may transmit the third data to the memory 300.

Therefore, the third data generated by the FBC 200 may be stored in thememory 300. Conversely, the third data stored in the memory 300 may beloaded by the multimedia IP 100 and transmitted to the FBC 200. The FBC200 may convert the third data into the second data by decompressing thethird data. The FBC 200 may transmit the second data to the multimediaIP 100.

That is, whenever the ISP 110, the G2D 120, the MFC 130, the GPU 140 andthe display 150 of the multimedia IP 100 access the memory 300individually, the FBC 200 may compress the second data into the thirddata and transmit the third data to the memory 300. For example, afterone of the components of the multimedia IP 100 generates and stores thesecond data in the memory 300, the frame buffer compressor 200 cancompress the stored data and store the compressed data into the memory300. Conversely, whenever a data request is made from the memory 300 tothe ISP 110, the G2D 120, the MFC 130, the GPU 140 and the display 150of the multimedia IP 100, the FBC 200 may decompress the third data intothe second data and transmit the second data to each of the ISP 110, theG2D 120, the MFC 130, the GPU 140 and the display 150 of the multimediaIP 100.

The memory 300 may store the third data generated by the FBC 200 andprovide the stored third data to the FBC 200 so that the FBC 200 candecompress the third data.

In an embodiment, the system bus 400 is connected to each of themultimedia IP 100 and the memory 300. Specifically, the ISP 110, the G2D120, the MFC 130, the GPU 140 and the display 150 of the multimedia IP100 may be individually connected to the system bus 400. The system bus400 may serve as a path through which the ISP 110, the G2D 120, the MFC130, the GPU 140 and the display 150 of the multimedia IP 100 and thememory 300 exchange data with each other.

In an embodiment, the FBC 200 is not connected to the system bus 400 andconverts the second data into the third data or the third data into thesecond data when each of the ISP 110, the G2D 120, the MFC 130, the GPU140 and the display 150 of the multimedia IP 100 accesses the memory300.

FIG. 2 is a detailed block diagram of the FBC 200 illustrated in FIG. 1.

Referring to FIG. 2 , the FBC 200 includes an encoder 210 (e.g., anencoding circuit) and a decoder 220 (e.g., a decoding circuit).

The encoder 210 may receive the second data from the multimedia IP 100and generate the third data. Here, the second data may be transmittedfrom each of the ISP 110, the G2D 120, the MFC 130, the GPU 140 and thedisplay 150 of the multimedia IP 100. The third data may be transmittedto the memory 300 through the multimedia IP 100 and the system bus 400.

Conversely, the decoder 220 may decompress the third data stored in thememory 300 into the second data. The second data may be transmitted tothe multimedia IP 100. Here, the second data may be transmitted to eachof the ISP 110, the G2D 120, the MFC 130, the GPU 140 and the display150 of the multimedia IP 100.

FIG. 3 is a detailed block diagram of the encoder 210 illustrated inFIG. 2 .

Referring to FIG. 3 , the encoder 210 includes a rust mode selector 219(e.g., a mode selection circuit), a prediction module 211 (e.g., a logiccircuit), a quantization module 213 (e.g., a logic circuit), an entropyencoding module 215 (e.g., a logic circuit), and a padding module 217(e.g., a logic circuit).

In an embodiment, the first mode selector 219 determines whether theencoder 210 will operate in a lossless mode (e.g., lossless compression)or a lossy mode (e.g., lossy compression). When the encoder 210 operatesin the lossless mode based on the determination result of the first modeselector 219, the second data may be compressed along a lossless path ofFIG. 3 . When the encoder 210 operates in the lossy mode, the seconddata may be compressed along a lossy path.

The first mode selector 219 may receive a signal from the multimedia IP100 used to determine whether lossless compression or lossy compressionwill be performed. Here, the lossless compression denotes compressionwithout loss of data and has a compression ratio that varies dependingon data. On the other hand, the lossy compression denotes compression inwhich data is partially lost. The lossy compression has a highercompression ratio than the lossless compression and has a preset fixedcompression ratio.

In the case of the lossless mode, the first mode selector 219 enablesthe second data to flow to the prediction module 211, the entropyencoding module 215 and the padding module 217 along the lossless path.Conversely, in the case of the lossy mode, the first mode selector 219enables the second data to flow to the prediction module 211, thequantization module 213 and the entropy encoding module 215 along thelossy path.

The prediction module 211 converts the second data into predicted imagedata. The predicted image data is a compressed representation of thesecond data as a combination of prediction data and residual data. In anembodiment, the prediction data is image data of one pixel of the imagedata and the residual data is created from the differences between theprediction data and the image data of the pixels of the image data thatare adjacent the one pixel. For example, if the image data of the onepixel has a value of 0 to 255, 8 bits may be needed to represent thevalue. When the adjacent pixels have similar values to that of the onepixel, the residual data of each of the adjacent pixels is much smallerthan the prediction data. For example, if an adjacent pixel has asimilar value, only a difference (i.e., a residual) from the value ofthe adjacent pixel can be represented without loss of data, and thenumber of bits of data needed to represent the difference may be muchsmaller than 8 bits. For example, when pixels having values of 253, 254and 255 are arranged successively, if prediction data is 253, a residualdata representation of (253(prediction), 1(residual), 2(residual)) maybe sufficient, and the number of bits per pixel needed for this residualdata representation may be 2 bits which is much smaller than 8 bits. Forexample, 24 bits of data of 253, 254, and 255 can be reduced to 12 bitsdue to 8 bit prediction data of 253 (11111101), 2 bit residual data of254−251=1 (01), and 2 bit residual data of 255−253=2 (10).

The prediction module 211 may compress the overall size of the seconddata by dividing the second data into prediction data and residual data.Various methods can be used to determine the prediction data. Specificprediction methods will be described in more detail later.

The prediction module 211 may perform prediction on a pixel-by-pixelbasis or on a block-by-block basis. Here, a block may be an area formedby a plurality of adjacent pixels. For example, prediction on a pixelbasis could mean that all the residual data is created from one of thepixels, and prediction on the block basis could mean that residual datais created for each block from a pixel of the corresponding block.

The quantization module 213 may further compress the predicted imagedata into which the second data has been compressed by the predictionmodule 211. The quantization module 213 may remove lower bits of thepredicted image data using a preset quantization parameter (QP). Forexample, if the prediction data is 253 (11111101), the prediction datacan be reduced from 8 bits to 6 bits by removing the lower 2 bits, whichresults in prediction data of 252 (111111). Specifically, arepresentative value may be selected by multiplying the data by a QP,where numbers below a decimal point are discarded, thus causing a loss.If pixel data has a value of 0 to 2⁸−1 (=255), the QP may be defined as1/(2^(n)−1) (where n is an integer of 8 or less). However, the currentembodiments are not limited to this case.

Here, since the removed lower bits are not restored later, they arelost. Therefore, the quantization module 213 is utilized only in thelossy mode. The lossy mode may have a relatively higher compressionratio than the lossless mode and may have a preset fixed compressionratio. Therefore, information about the compression ratio is not neededlater.

The entropy encoding module 215 may compress, through entropy coding,the predicted image data compressed by the quantization module 213 inthe lossy mode or the predicted image data into which the second datahas been compressed by the prediction module 211 in the lossless mode.In the entropy coding, the number of bits may be allocated according tofrequency.

In an embodiment, the entropy encoding module 215 compresses thepredicted image data using Huffman coding. In an alternative embodiment,the entropy encoding module 215 compresses the predicted image datathrough exponential Golomb coding or Golomb rice coding. In anembodiment, the entropy encoding module 215 generates a table using kvalues, and compresses the predicted image data using the generatedtable. The k values may be entropy encoding/coding values using in theentropy encoding.

The padding module 217 may perform padding on the predicted image datacompressed by the entropy encoding module 215 in the lossless mode.Here, the padding may refer to adding meaningless data in order to fit aspecific size.

The padding module 217 may be activated not only in the lossless modebut also in the lossy mode. In the lossy mode, the predicted image datamay be compressed by the quantization module 213 more than an intendedcompression ratio. In this case, the predicted image data may be passedthrough the padding module 217 even in the lossy mode, converted intothe third data and then transmitted to the memory 300. In an exemplaryembodiment, the padding module 217 is omitted so that no padding isperformed.

A compress manager 218 determines a combination of a QP table and anentropy table used for quantization and entropy coding, respectively,and controls the compression of the second data according to thedetermined combination of the QP table and the entropy table.

In this case, the first mode selector 219 determines that the encoder210 will operate in the lossy mode. Accordingly, the second data iscompressed along the lossy path of FIG. 3 . That is, the compressmanager 218 determines that a combination of a QP table and an entropytable is needed and compresses the second data according to thedetermined combination of the QP table and the entropy table based onthe premise that the FBC 200 compresses the second data using a lossycompression algorithm.

Specifically, the QP table may include one or more entries, and each ofthe entries may include a QP used to quantize the second data.

In an embodiment, the entropy table refers to a plurality of code tablesidentified by k values to perform an entropy coding algorithm. Theentropy table that can be used in some embodiments may include at leastone of an exponential Golomb code and a Golomb rice code.

The compress manager 218 determines a QP table including a predeterminednumber of entries, and the FBC 200 quantizes the predicted second datausing the determined QP table. In addition, the compress manager 218determines an entropy table using a predetermined number of k values,and the FBC 200 performs entropy coding on the quantized second datausing the determined entropy table. That is, the FBC 200 generates thethird data based on a combination of a QP table and an entropy tabledetermined by the compress manager 218.

Then, the FBC 200 may write the generated third data into the memory300. In addition, the FBC 200 may read the third data from the memory300, decompress the read third data, and provide the decompressed thirddata to the multimedia IP 100.

FIG. 4 is a detailed block diagram of the decoder 220 illustrated inFIG. 2 .

Referring to FIGS. 3 and 4 , the decoder 220 includes a second modeselector 229 (e.g., logic circuit), an unpadding module 227 (e.g., logiccircuit), an entropy decoding module 225 (e.g., logic circuit), aninverse quantization module 223 (e.g., logic circuit), and a predictioncompensation module 221 (e.g., logic circuit).

The second mode selector 229 determines whether the third data stored inthe memory 300 has been generated through lossless compression or lossycompression of the second data. In an exemplary embodiment, the secondmode selector 229 determines whether the third data has been generatedby compressing the second data in the lossless mode or the lossy modebased on the presence or absence of a header.

If the third data has been generated by compressing the second data inthe lossless mode, the second mode selector 229 enables the third datato flow to the unpadding module 227, the entropy decoding module 225 andthe prediction compensation module 221 along a lossless path.Conversely, if the third data has been generated by compressing thesecond data in the lossy mode, the second mode selector 229 enables thethird data to flow to the entropy decoding module 225, the inversequantization module 223 and the prediction compensation module 221 alonga lossy path.

The unpadding module 227 may remove a portion of data which has beenpadded by the padding module 217 of the encoder 210. The unpaddingmodule 227 may be omitted when the padding module 217 is omitted.

The entropy decoding module 225 may decompress data which has beencompressed by the entropy encoding module 215. The entropy decodingmodule 225 may perform decompression using Huffman coding, exponentialGolomb coding, or Golomb rice coding. Since the third data includes kvalues, the entropy decoding module 225 may perform decoding using the kvalues.

The inverse quantization module 223 may decompress data which has beencompressed by the quantization module 213. The inverse quantizationmodule 223 may restore the second data compressed by the quantizationmodule 213 using a predetermined quantization parameter (QP). Forexample, the inverse quantization module 223 may perform an inversequantization operation on the output of the entropy decoding module 225.However, the inverse quantization module 223 cannot completely recoverthe data lost in the compression process. Therefore, the inversequantization module 223 is utilized only in the lossy mode.

The prediction compensation module 221 may perform predictioncompensation to recover data represented as prediction data and residualdata by the prediction module 211. For example, the predictioncompensation module 221 may convert a residual data representation of(253(prediction), 1(residual), 2(residual)) into (253, 254, 255). Forexample, the prediction compensation module 221 may restore the data byadding the residual data to the prediction data. Specific predictioncompensation methods will be described in more detail later.

The prediction compensation module 221 may restore data predicted on apixel-by-pixel basis or a block-by-block basis by the prediction module211. Accordingly, the second data may be restored or the third data maybe decompressed and then transmitted to the multimedia IP 100.

A decompress manager 228 may perform an operation to appropriatelyreflect a combination of a QP table and an entropy table, which has beendetermined by the compress manager 218 to compress the second data asdescribed above with reference to FIG. 3 , in the decompression of thethird data.

When the prediction module 211 performs prediction on a pixel-by-pixelbasis, the prediction compensation module 221 may also performprediction compensation on a pixel-by-pixel basis. When prediction andprediction compensation are performed on a pixel-by-pixel basis, thereis no dependency between blocks. Therefore, random access of themultimedia IP 100 is possible. Here, the random access may refer todirectly accessing a necessary block instead of sequentially accessingblocks from a first block.

FIG. 5 illustrates the arrangement of pixels of image data of the imageprocessing device according to an exemplary embodiment of the inventiveconcept.

Referring to FIG. 5 , the prediction module 211 of the encoder 210 ofthe FBC 200 of the image processing device according to an exemplaryembodiment receives the second data (e.g., image data). The predictionmodule 211 converts the image data into predicted image data (e.g.,prediction data and residual data). The arrangement of the predictedimage data may be the same as the arrangement of the image data.However, unlike the image data, values of the predicted image data maybe reduced to residual data and displayed accordingly.

Here, the image data may be composed of a plurality of pixels arrangedin a plurality of rows and a plurality of columns. As illustrated inFIG. 5 , the image data may include a plurality of rows Row 1 throughRow 10 and a plurality of columns Column1 through Column10. Although tenrows Row 1 through Row 10 and ten columns Column 1 through Column 10 areillustrated in FIG. 5 , the current embodiments are not limited to thiscase.

That is, the number of rows and columns in which pixels are arranged inthe image data of the image processing device according to theembodiments can vary.

The rows Row 1 through Row 10 may include first through tenth rows Row 1through Row10. Here, the first row Row 1 is a top row, and the tenth rowRow 10 is a bottom row. That is, the second through tenth rows Row 2through Row 10 may be sequentially arranged below the first row Row 1.

The columns Column 1 through Column 10 may include first through tenthcolumns Column 1 through Column 10. Here, the first column Column 1 maybe a leftmost column, and the tenth column Column 10 may be a rightmostcolumn. That is, the second through tenth columns Column 2 throughColumn 10 may be sequentially arranged on a right side of the firstcolumn Column 1.

FIG. 6 illustrates pixels in the top row to explain the predictionperformed by the image processing device according to an exemplaryembodiment of the inventive concept.

Referring to FIGS. 3 through 5 and 6 , when performing prediction on apixel-by-pixel basis, the prediction module 211 sequentially performsprediction in a downward direction from the first row Row 1 to the tenthrow Row 10. However, the current embodiments are not limited to thiscase, and the image processing device according to at least one of theembodiments can perform prediction on a row-by-row basis but in adifferent order. For ease of description, it will be assumed that theprediction module 211 performs prediction on a row-by-row basis in thedownward direction.

Likewise, when performing prediction compensation on a pixel-by-pixelbasis, the prediction compensation module 221 may sequentially performprediction compensation in the downward direction from the first row Row1 to the tenth row Row 10. However, the current embodiments are notlimited to this case, and the image processing device according to atleast one of the embodiments can perform prediction compensation on arow-by-row basis, but in a different order. For ease of description, itwill be assumed that the prediction module 211 performs predictioncompensation on a row-by-row basis in the downward direction.

The first row Row 1 may include first through tenth pixels X1 throughX10 arranged sequentially from the left. That is, the first pixel X1 maybe located on a leftmost side of the first row Row 1, and the secondthrough tenth pixels X2 through X10 may be sequentially disposed on theright side of the first pixel X1.

In an exemplary embodiment, the prediction module 211 divides the firstrow Row 1 into a plurality of groups. Specifically, the first row Row 1may include a first group G1, a second group G2, and a third group G3.Here, the first group G1 and the second group G2 each include fourpixels. The third group G3 includes two pixels since the number ofremaining pixels in the first row Row 1 is only two.

The number of pixels in each group may be a preset value. The number ofpixels in all groups may be the same, except for a group having aninsufficient number of pixels as in the third group G3. In FIG. 6 , thenumber of pixels in each group is four. However, this is merely anexample, and the number of pixels in each group can vary. However, thenumber of pixels in each group should be less than or equal to the totalnumber of pixels in the first row Row 1 (e.g., 10).

In an embodiment, the prediction module 211 sets prediction data of thefirst pixel X1 to half of the bit depth. However, the currentembodiments are not limited to this case. For example, if the bit depthis 8 bits, half of the bit depth would be 128. Thus, if the first pixelX1 is 253, and the prediction data is 128, then the residual data wouldbe 253−128=125.

The prediction performed by the prediction module 211 refers to dividinga data value of a pixel into prediction data and residual data. That is,the prediction module 211 may perform a prediction on the first pixel X1by obtaining prediction data of the first pixel X1 and setting adifference between a data value of the first pixel X1 and the predictiondata as residual data.

Similarly, the prediction compensation performed by the predictioncompensation module 221 may refer to obtaining the original pixel value,that is, a data value included in a pixel by using prediction data andresidual data. For example, the original pixel value can be obtained byadding the residual data to the prediction data.

Basically, the purpose of performing prediction is to represent a datavalue with fewer bits using a similar, neighboring data value. However,since the first pixel X1 is a pixel for which a neighboring value cannotbe used because it is a first pixel to be predicted, half of the bitdepth may be used as the prediction data.

Then, the prediction module 211 may perform prediction on the secondpixel X2. The prediction module 211 obtains prediction data and residualdata by using the value of the first pixel X1 as the prediction data inthe prediction of the second pixel X2. For example, if the first pixelX1 is 253 and the second pixel X2 is 254, then the prediction data forthe second pixel X2 is 253 and residual data for the second pixel X2 is254−253=1. Similarly, the third pixel X3 uses the value of the secondpixel X2 as prediction data, and the fourth pixel X4 uses the value ofthe third pixel X3 as prediction data.

That is, in the first group G1, the prediction module 211 may performprediction using differential pulse-code modulation (DPCM) in which thevalue of a left pixel is used as prediction data as described above.

When performing prediction of the second group G2, the prediction module211 uses the data value of the first pixel X1, which is the first pixelof the first group G1, as prediction data of the fifth pixel X5.Likewise, the prediction module 211 uses the value of the fifth pixelX5, which is the first pixel of the second group G2, as prediction dataof the ninth pixel X9 of the third group G3.

That is, within each group, the prediction module 211 may use the DPCM,in other words, uses the value of an immediately left pixel asprediction data. However, as for the first pixel of each group, theprediction module 211 uses the value of the first pixel of a previousgroup as prediction data.

This is related to the fact that the prediction compensation of theprediction compensation module 221 is performed in a sequential manner.If the prediction module 211 does not perform grouping and uses thevalue of an immediately left pixel as prediction data of each pixelexcept for the first pixel X1 of the first row Row 1, the predictioncompensation module 221 cannot obtain the value of the fifth pixel X5until identifying the value of the fourth pixel X4 through predictioncompensation.

In this case, the prediction compensation module 221 can performprediction compensation only sequentially from the first pixel X1 to thetenth pixel X10. However, since the prediction module 211 and theprediction compensation module 221 according to an exemplary embodimentof the inventive concept is capable of parallel processing. Ifsequential prediction compensation can be performed in parallel, fasterprediction compensation is possible.

The prediction module 211 of the image processing device according to anexemplary embodiment of the inventive concept does not need to waituntil prediction compensation on the pixels of the first group G1 isfinished in order to perform prediction on the pixels of the secondgroup G2. In an exemplary embodiment, the FBC 200 or more specificallythe prediction compensation module 221 includes multiple circuits orprocessors (e.g., pipeline stages), where each circuit/processorperforms independent prediction compensation, and the operation of thesecircuits are staggered. For example, a first one of thesecircuits/processors (e.g., a first stage of a pipeline) beginsperforming prediction compensation on the first group G1, and then afterthe first circuit has finished processing the first pixel X1 of thefirst group G1, the second circuit (e.g., a second stage of thepipeline) begins performing prediction compensation on the second groupG2. The processing of the first pixel X1 by the first circuit mayinclude adding residual data of the first pixel X1 to the predictiondata generated from the one-half bit depth to determine the originaldata of the first pixel X1 and passing the original data of the firstpixel to the second circuit as prediction data to be used in the secondcircuit's generation of the original data of the fifth pixel X5. Then,the second circuit/processor can generate the original data of the fifthpixel X5 by adding the residual data of the fifth pixel X5 to thereceived prediction data.

FIG. 7 illustrates, in a time-series manner, the order in whichprediction compensation is performed on the pixels of FIG. 6 .

Referring to FIG. 7 , the prediction compensation module 221 performsprediction compensation on the first pixel X1 at a first time t0. Here,since the first pixel X1 has half of the bit depth as prediction data asdescribed above, the prediction compensation may be performed regardlessof the values of other pixels.

The prediction compensation module 221 obtains the value of the secondpixel X2 of the first group G1 by performing prediction compensation onthe second pixel X2 using the value of the first pixel X1 obtainedthrough the prediction compensation on the first pixel PX1. That is,since the value of the second pixel X2 uses the value of the first pixelX1 as prediction data, it may be obtained as the sum of residual dataand the prediction data. The prediction compensation module 221 obtainsthe value of the second pixel X2 at a second time t1.

The prediction compensation module 221 also obtains the value of thefifth pixel X5 of the second group G2 through prediction compensation.This is because the fifth pixel X5 also uses the value of the firstpixel X1 as prediction data. Therefore, it is possible to immediatelyperform prediction compensation on the fifth pixel X5 of the secondgroup G2 at the second time t1 without the need to wait until predictioncompensation on all pixels of the first group G1 have finished. Forexample, a first stage of a pipeline can operate on the first pixel X1at time t0 to generate the original data of the first pixel to be usedas first prediction data for the second pixel X1 and the fifth pixel X5,and then a second stage of the pipeline can operate on the fifth pixelX5 at time 1 since the first stage generated the first prediction datait needs for generating the original data of the fifth pixel X5 at time0.

Similarly, the prediction compensation module 221 may perform predictioncompensation on the ninth pixel X9 of the third group G3 at a third timet2. Since the value of the fifth pixel X5 has already been obtained atthe second time t1, the prediction compensation module 221 can obtainthe value of the ninth pixel X9 using the value of the fifth pixel X5.For example, the second stage of the pipeline uses the first predictiondata it received from the first pipeline to generate the original dataof the fifth pixel at time t1 to be used as second prediction data forthe third pixel X3 and the ninth pixel X9, and then a third stage of thepipeline can operate on the ninth pixel X9 at time t2 since the secondstage generated the second prediction data it needs for generating theoriginal data of the ninth pixel X9. In an embodiment, the predictioncompensation module 221 also performs prediction compensation on thethird pixel X3 and the sixth pixel X6 in parallel.

That is, the prediction compensation module 221 of the image processingdevice according to an exemplary embodiment of the inventive concept canperform parallel prediction compensation because the prediction module211 does not perform prediction on each group in a serial manner.

FIG. 8 illustrates pixels in a row other than the top row to explain theprediction of the image processing device according to embodiments ofthe inventive concept.

Referring to FIG. 8 , the second row Row 2 includes eleventh throughtwentieth pixels X11 through X20 arranged sequentially from the left.That is, the eleventh pixel X11 is located on the leftmost side of thesecond row Row 2, and the twelfth through twentieth pixels X12 throughX20 may be sequentially disposed on the right side of the eleventh pixelX11.

The prediction module 211 divides the second row Row 2 into a pluralityof groups. Specifically, the second row Row 2 includes a fourth groupG4, a fifth group G5, and a sixth group G6. Here, the fourth group G4and the fifth group G5 each include four pixels. The sixth group G6includes two pixels since the number of remaining pixels in the secondrow Row 2 is only two. The number of pixels in each group can vary.

The prediction module 211 performs prediction on the eleventh pixel X 11which is a first pixel of the fourth group G4. In an embodiment of theinventive concept, the prediction of the eleventh pixel X11 is performedusing the value of the first pixel X1 as prediction data. Since there isno pixel that can be referred to on the left side of the eleventh pixelX11 and a pixel closest to the eleventh pixel X11 is the first pixel X1above the eleventh pixel X11, it may be efficient to use the value ofthe first pixel X1 as the prediction data.

Then, the prediction module 211 performs prediction on the twelfth pixelX12. The prediction module 211 may obtain prediction data and residualdata by using the value of the eleventh pixel X 11 as the predictiondata in the prediction of the twelfth pixel X12. Likewise, thethirteenth pixel X13 uses the value of the twelfth pixel X12 asprediction data, and the fourteenth pixel X14 uses the value of thethirteenth pixel X13 as prediction data. That is, in the fourth groupG4, the prediction module 211 performs prediction using the DPCM inwhich the value of a left pixel is used as prediction data as describedabove.

Likewise, the prediction module 211 performs prediction on the fifteenthpixel X15, which is a first pixel of the fifth group G5, by using thevalue of the fifth pixel X5 located above the fifteenth pixel X15 asprediction data. This is not only efficient because the fifth pixel X5is adjacent to the fifteenth pixel X15, but also intended for parallelexecution of prediction compensation. Similarly, the prediction module211 performs prediction on the nineteenth pixel X19, which is a firstpixel of the sixth group G6, using the value of the ninth pixel X9 abovethe nineteenth pixel X19 as prediction data.

Prediction on other pixels in the fifth group G5 and the sixth group G6may also be performed by the prediction module 211 using the DPCM.

FIG. 9 illustrates, in a time-series manner, the order in whichprediction compensation is performed on the pixels of FIG. 8 .

Referring to FIG. 9 , the prediction compensation module 221simultaneously performs prediction compensation on the eleventh pixelX11, the fifteenth pixel X15 and the nineteenth pixel X19 in parallel ata first time t0. Since the eleventh pixel X11, the fifteenth pixel X15and the nineteenth pixel X19 in the second row Row 2 use the values ofthe pixels in the first row Row 1 as prediction data, predictioncompensation on the eleventh pixel X 11, the fifteenth pixel X15 and thenineteenth pixel X19 can be performed immediately regardless of thevalues of other pixels in the second row Row 2. For example, a firststage of the prediction compensation module 221 may operate on pixelX11, a second stage of the prediction compensation module 221 mayoperate on pixel X15, and a third stage of the prediction compensationmodule 221 may operate on pixel X19, at the same time t0, assuming thedata for pixels of the prior row was already previously decompressed.For example, decompression of the data of the prior row provides thevalue of pixel X1 used to generate first prediction data, the value ofpixel X5 used to generate second prediction data, and the value of pixelX9 used to generate third prediction data, where the first predictiondata is added to the residual data of pixel X 11 to restore the data ofpixel X11, where the second prediction data is added to the residualdata of pixel X15 to restore the data of pixel X15, and the thirdprediction data is added to the residual data of pixel X19 to restorethe data of pixel X19.

Then, the prediction compensation module 221 sequentially performsprediction compensation on the pixels of the fourth group G4, the fifthgroup G5 and the sixth group G6 using the values of the eleventh pixel X11, the fifteenth pixel X15 and the nineteenth pixel X19 obtainedthrough the prediction compensation.

An image processing device according to at least one embodiment of theinventive concept will now be described with reference to FIGS. 1through 4 and 10 .

FIG. 10 illustrates pixels in a row other than a top row to explain theprediction of an image processing device according to an exemplaryembodiment of the inventive concept.

Referring to FIGS. 1 through 4 and 10 , a prediction module 211 of theimage processing device according to an embodiment performs predictionon a first pixel of each group by considering at least one of an upperpixel, an upper left pixel and an upper right pixel.

Specifically, when the prediction module 211 performs prediction on afifteenth pixel X15, it obtains prediction data using at least one of afifth pixel X5 located above the fifteenth pixel X15, a fourth pixel X4located on a left side of the fifth pixel X5, and a sixth pixel X6located on a right side of the fifth pixel X5.

That is, the prediction data of the fifteenth pixel X15 may be any oneof the values of the fourth pixel X4, the fifth pixel X5 and the sixthpixel X6, may be a value calculated using two of the values of thefourth pixel X4, the fifth pixel X5 and the sixth pixel X6, or may be avalue calculated using all of the values of the fourth pixel X4, thefifth pixel X5 and the sixth pixel X6. For example, the prediction datacan be generated by averaging the two values together or averaging thethree values together.

Accordingly, for the fifteenth pixel X15, the prediction module 211 canobtain prediction data having higher efficiency by using more diversesources. Likewise, prediction data of a nineteenth pixel X19 can beobtained in consideration of at least one of the values of an eighthpixel X8, a ninth pixel X9 and a tenth pixel X10. Similarly, predictiondata of an eleventh pixel X11 may also be obtained in consideration ofat least one of the values of a first pixel X1 and a second pixel X2.Here, since there is no pixel on the left side of the first pixel X1,only two pixels can be considered.

While there are various methods of performing prediction by consideringa plurality of pixels, context prediction to be described later can alsobe used.

An image processing device according to an embodiment of the inventiveconcept will now be described with reference to FIGS. 1 through 4 and 11through 16 .

FIG. 11 illustrates the arrangement of pixels to explain predictionperformed within a group by an image processing device according to anembodiment of the inventive concept.

Referring to FIGS. 1 through 4 and 11 , a prediction module 211 of theimage processing device according to an embodiment performs contextprediction within a group. The context prediction is a method ofgenerating a context using a plurality of neighboring pixels anddetermining prediction data based on the generated context.

Specifically, prediction and prediction compensation may be performed ona twelfth pixel X12 of a second row Row 2 using an eleventh pixel X11which is a left pixel, a second pixel X2 which is an upper pixel, afirst pixel X1 which is an upper left pixel, and a third pixel X3 whichis an upper right pixel.

Since a prediction compensation module 221 sequentially performsprediction compensation in a downward direction from a first row Row1,when prediction compensation is performed on the twelfth pixel X12 ofthe second row Row 2, the values of all pixels of the first row Row 1have already been obtained through prediction compensation. In addition,since the eleventh pixel X11 located on the left side of the twelfthpixel X12 in the second row Row 2 is a pixel within the same group asthe twelfth pixel X12, the value of the eleventh pixel X11 may havealready been obtained through prediction compensation.

On the other hand, since a thirteenth pixel X13 and a fourteenth pixelX14 located on the right side of the twelfth pixel X12 within the samegroup have not yet undergone prediction compensation, the values of thethirteenth pixel X13 and the fourteenth pixel X14 cannot be referred toin the prediction compensation of the twelfth pixel X12.

In an embodiment, the prediction module 211 include a branch statement211 a, a lookup table 211 b, and a prediction equation 211 c.

The branch statement 211 a may receive the values of pixels to bereferred to, that is, the values of the first pixel X1, the second pixelX2, the third pixel X3 and the eleventh pixel X11 in the case of thetwelfth pixel X12. The branch statement 211 a may generate a context ctxusing the values of the first pixel X1, the second pixel X2, the thirdpixel X3 and the eleventh pixel X11. The branch statement 211 a maytransmit the context ctx to the lookup table 211 b.

The lookup table 211 b may receive the context ctx and output groupinformation Gr. The group information Gr may be information thatdetermines which equation included in the prediction equation 211 cshould be used.

The prediction equation 211 c may receive the group information Gr andproduce prediction data Xp and a residual r using an equationcorresponding to the group information Gr.

FIG. 13 is a detailed diagram of the branch statement 211 a illustratedin FIG. 12 . FIG. 14 is a conceptual diagram for structurally explainingthe operation of the branch statement 211 a of FIG. 13 .

Referring to FIG. 13 , the branch statement 211 a may include aplurality of branch statements. Although five branch statements areillustrated in FIG. 13 , the current embodiments are not limited to thiscase.

X1, X2, X3 and X 11 specified in the branch statement 211 a indicate thevalues of the first pixel X1, the second pixel X2, the third pixel X3and the eleventh pixel X11, respectively. If pixels referred to arechanged, the branch statement 211 a may also be changed. That is, forthe sake of convenience, the branch statement 211 a of FIG. 13 iscreated based on the assumption that the first pixel X1, the secondpixel X2, the third pixel X3 and the eleventh pixel X11 are input.

A first branch statement {circle around (1)} defines that the contextctx is 1 if the absolute value of a difference between the value of theeleventh pixel X11 and the value of the second pixel X2 is larger than10 and is 0 if not.

A second branch statement {circle around (2)} defines that the contextctx is doubled (“<<1” is a bitwise operation of binary numbers andrepresents double as a number) and then 1 is added to the doubledcontext ctx if the value of the eleventh pixel X11 is larger than thevalue of the second pixel X2 and that the context ctx is only doubled ifnot.

A third branch statement {circle around (3)} defines that the contextctx is doubled and then 1 is added to the doubled context ctx if thevalue of the eleventh pixel X11 is larger than the value of the firstpixel X1 and that the context ctx is only doubled if not.

A fourth branch statement {circle around (4)} defines that the contextctx is doubled and then 1 is added to the doubled context ctx if thevalue of the second pixel X2 is larger than the value of the first pixelX1 and that the context ctx is only doubled if not.

A fifth branch statement {circle around (5)} defines that the contextctx is doubled and then 1 is added to the doubled context ctx if thevalue of the second pixel X2 is larger than the value of the third pixelX3 and that the context ctx is only doubled if not.

Referring to FIG. 14 , the context ctx may have 2⁵=32 values through atotal of five branch statements. When the number of branch statements ischanged, the number of values of the context ctx may also be changed.That is, the context ctx can have a total of 32 values ranging from 0 to31.

Specifically, the context ctx may branch into 0 and 1 through the firstbranch statement {circle around (1)} and may branch into a total of fourvalues ranging from 0 to 3 through the second branch statement {circlearound (2)}. The context ctx may branch into a total of eight valuesranging from 0 to 7 through the third branch statement {circle around(3)} and may branch into a total of 16 values ranging from 0 to 15through the fourth branch statement {circle around (4)}. Finally, thecontext ctx may branch into a total of 32 values ranging from 0 to 31through the fifth branch statement {circle around (5)}.

FIG. 15 illustrates the lookup table 211 b of FIG. 12 .

Referring to FIG. 15 , the lookup table 211 b may have a table of groupinformation values corresponding to context values. In FIG. 15 ,ctxPredLookUpLuma denotes a lookup table for a luma signal block of YUVdata, and ctxPredLookUpChroma denotes a lookup table for a chroma signalblock of YUV data.

That is, the context ctx may branch into values ranging from 0 to 31,and the group information Gr corresponding to the context ctx may havesix values ranging from 0 to 5 as illustrated in FIG. 15 . Here, thenumber of values of the group information Gr can vary as needed. Thenumber of values of the group information Gr may correspond to thenumber of equations included in the prediction equation 211 c.

FIG. 16 is a detailed diagram of the prediction equation 211 cillustrated in FIG. 12 .

Referring to FIG. 16 , the prediction equation 211 c may includeequations for Xp0, Xp1, Xp2, Xp3, Xp4 and Xp5 corresponding to sixvalues of the group information Gr ranging from 0 to 5. Specifically,when the group information Gr is 0, 1, 2, 3.4 and 5, Xp0, Xp1. Xp2, Xp3,Xp4 and Xp5 may be employed as Xp, that is, the prediction data Xp. Ifthe group information Gr is 0. Xp0 may be the prediction data Xp.Accordingly, the residual r may be a difference (i.e., Xp0) between adata value X of a pixel and the prediction data Xp.

When performing prediction within a group, the prediction module 211 ofthe image processing device according to at least one embodiment of theinventive concept can perform more accurate and reliable prediction byconsidering the relationship with neighboring pixels using contextprediction. If prediction data is precisely obtained, the differencebetween the prediction data and the value of a pixel, that is, aresidual can be represented by less bits. Therefore, the efficiency ofdata compression can be increased.

An image processing device according to an exemplary embodiment of theinventive concept will now be described with reference to FIG. 17 .

FIG. 17 is a block diagram of an image processing device according to anexemplary embodiment of the inventive concept.

Referring to FIG. 17 , an FBC 200 of the image processing deviceaccording to the embodiments is directly connected to a system bus 400.

The FBC 200 is not directly connected to a multimedia IP 100 but isconnected to the multimedia IP 100 through the system bus 400.Specifically, each of an ISP 110, a G2D 120, an MFC 130, a GPU 140 and adisplay 150 of the multimedia IP 100 may exchange data with the FBC 200through the system bus 400 and transmit data to a memory 300 through thesystem bus 400.

That is, in a compression process, each of the ISP 110, the G2D 120, theMFC 130, the GPU 140 and the display 150 of the multimedia IP 100 maytransmit the second data to the FBC 200 through the system bus 400.Then, the FBC 200 may compress the second data into the third data andtransmit the third data to the memory 300 through the system bus 400.

Similarly, in a decompression process, the FBC 200 may receive the thirddata stored in the memory 300 through the system bus 400 and decompressthe third data into the second data. Then, the FBC 200 may transmit thesecond data to each of the ISP 110, the G2D 120, the MFC 130, the GPU140 and the display 150 of the multimedia IP 100 through the system bus400.

In the current embodiment, although the FBC 200 is not individuallyconnected to the ISP 110, the G2D 120, the MFC 130, the GPU 140 and thedisplay 150 of the multimedia IP 100, it can still be connected to theISP 110, the G2D 120, the MFC 130, the GPU 140 and the display 150 ofthe multimedia IP 100 through the system bus 400. Therefore, hardwareconfiguration can be simplified, and operation speed can be improved.

An image processing device according to an embodiment of the inventiveconcept will now be described with reference to FIG. 18 .

FIG. 18 is a block diagram of an image processing device according to anexemplary embodiment of the inventive concept.

Referring to FIG. 18 , the image processing device according to anexemplary embodiment is configured such that a memory 300 and a systembus 400 are connected to each other through an FBC 200.

That is, the memory 300 is not directly connected to the system bus 400and is connected to the system bus 400 only through the FBC 200. Inaddition, an ISP 110, a G2D 120, an MFC 130, a GPU 140 and a display 150of a multimedia IP 100 may be directly connected to the system bus 400.Therefore, the ISP 110, the G2D 120, the MFC 130, the GPU 140 and thedisplay 150 of the multimedia IP 100 can access the memory 300 onlythrough the FBC 200.

Since the FBC 200 is involved in all accesses to the memory 300 in thecurrent embodiment, the FBC 200 may be directly connected to the systembus 400, and the memory 300 may be connected to the system bus 400through the FBC 200. This can reduce errors in data transmission andimprove speed.

What is claimed is:
 1. An image processing device for processing animage data which includes a plurality of pixels arranged in a pluralityof rows and a plurality of columns, the image processing devicecomprising: a processor which configured to, divide each row into aplurality of groups sequentially arranged with each other, each groupincludes a same number of pixels, perform prediction on a first pixel ofevery group in a first row based on at least one previous predictedpixel located within a second other row at a first time, and performprediction on a second pixel of every group in the first row based onlyon the first pixel of every group in the first row at a second timewhich is a sequential time of the first time, wherein the first row isone of among the plurality of rows, except a top row.
 2. The imageprocessing device of claim 1, wherein one group among the plurality ofgroups includes remaining pixels to subtract pixels of previous groupsin a corresponding row.
 3. The image processing device of claim 1,wherein the previous predicted pixel includes at least one of upperpixels of a corresponding group in an upper row.
 4. The image processingdevice of claim 1, wherein the processor is configured to, setprediction data of a first pixel of a first group in the top row to halfof a bit depth at a third time before the first time, and performprediction on a second pixel of a first group in the top row based onthe first pixel of the first group in the top row after the third time.5. The image processing device of claim 4, wherein the processor isconfigured to, perform prediction compensation on the first pixel of thefirst group in the top row to have the bit depth, perform predictioncompensation on the second to last pixel of the first group in the toprow base on the previous compensated pixel of the first group in the toprow, perform prediction compensation on the first pixel of a next groupin the top row based on the first pixel of a previous group in the toprow, perform prediction compensation on the second to last pixel of thenext group in the top row based on the left side pixel of thecorresponding pixel in the next group of the top row.
 6. The imageprocessing device of claim 3, wherein the perform of the prediction onthe first pixel performs a prediction based on at least one of an upperpixel of the first pixel of the corresponding group in the first row, aleft-side pixel of the upper pixel and the right side pixel of the upperpixel.
 7. The image processing device of claim 3, wherein the performingprediction on the first pixel of the first group in the first row isconfigured to, perform prediction based on at least one upper pixel ofthe first pixel of the first group in the first row and a right-sidepixel of the upper pixel.
 8. The image processing device of claim 1,wherein the processor is configured to, perform prediction on a secondto last pixel in each of the groups using the previous predicted pixelfor differential pulse-code modulation.
 9. An image processing devicefor processing an image data which includes a plurality of pixelsarranged in a plurality of rows and a plurality of columns, the imageprocessing device comprising: a processor, which configured to, divideeach row into a plurality of groups sequentially arranged with eachother, each group includes same number of pixels, perform prediction ona first pixel of a every group in a first row based only on at least oneof an upper pixel of the first pixel in a second other row and aright-side pixel of the upper pixel in the second other row at a firsttime, perform prediction on a second pixel of every group in the firstrow based only on the first pixel of a corresponding group in the firstrow at a second time, wherein the first row is one of among theplurality of rows, except a top row.
 10. The image processing device ofclaim 9, wherein the prediction on the second pixel is further based onat least one predicted upper pixel in an upper row of the first row. 11.The image processing device of claim 9, wherein the processor isconfigured to: generate a context of a current pixel of the currentgroup in a current row using the values of the previous predicted pixelsin the current row and an adjacent row, output group informationcorresponding the context in a lookup table, and produce a predictiondata and a residual using a predetermined equation corresponding to thegroup information.
 12. The image processing device of claim 11, whereinthe image processing device comprises: a prediction module including aplurality of branch statements, each branch statement generates thecontext of the current pixel using the value of the previous predictedpixel, wherein the prediction module comprises: the lookup tableincluding a plurality of group information corresponding to the context;and a plurality of predetermined equations corresponding to theplurality of group information respectively.
 13. The image processingdevice of claim 11, wherein the lookup table comprises a lookup tablefor a luma signal block of YUV data and a lookup table for a chromasignal block of YUV data.
 14. An image processing device for processingan image data which includes a plurality of pixels arranged in aplurality of rows and a plurality of columns, the image processingdevice comprising: a processor, wherein each row of the plurality ofrows includes a plurality of groups, wherein the processor is configuredto, perform prediction on a first pixel of every group in a current rowbased on an upper pixel in an upper row above the current row at a firsttime, and perform prediction on respective remaining pixels in everygroup in the current row based only on the first pixel or only on aprevious predicted pixel in the corresponding group of the current rowsequentially after the first time, wherein the current row is one ofamong the plurality of rows, except a top row.
 15. The image processingdevice of claim 14, wherein the previous predicted pixel furtherincludes at least one upper pixel of the one of the remaining pixels.16. The image processing device of claim 15, wherein when performingprediction on pixels in the top row, the processor is configured to,perform prediction on a first pixel of a first group to half of a bitdepth at a second time before the first time, and perform prediction onsecond to last pixels of the first group in the top row based on arespective left-side pixel of the first group in the top rowsequentially after the second time.
 17. The image processing device ofclaim 16, wherein the processor is configured to, perform prediction ona first pixel of a second to a last group in the top row based on thefirst pixel of the previous group in the top row, and perform predictionon respective remaining pixels of the second to the last group in thetop row based on a respective left-side pixel of the corresponding groupin the top row.